LITERATURE REVIEW

In the article “Credit Risk Rating at Large U.S. Banks” authors William F. Treacy and Mark S. Care say that risk ratings are the primary summary indicator of risk for banks’ individual credit exposures. They both shape and reflect the nature of credit decisions that banks make daily. The specifics of internal rating system architecture and operation differ substantially across banks. The number of grades and the risk associated with each grade vary across institutions, as do decisions about who assigns ratings and about the manner in which rating assignments are reviewed. In general, in designing rating systems, bank management must weigh numerous considerations, including cost, efficiency of information gathering, consistency of ratings produced, staff incentives, the nature of the bank’s business, and the uses to be made of internal ratings.

RATINGS MIGRATION SYSTEM

An Internal Ratings Migration Study by Michel Araten, Michael Jacobs Jr., Peeyush Varshney, and Claude R. Pellegrino-- This article discusses issues in evaluating banks’ internal ratings of borrowers. Ratings migration analysis entails the actuarial estimation of transition probabilities for obligor credit risk ratings, with emphasis on estimation of empirical default probabilities. Measurement of changes in borrower credit quality over time is important as obligor risk ratings are a key component of a bank’s credit capital methodology. These analyses permit banks to more accurately assess and price credit risk, as well as improve their assessment of loss reserves and portfolio capital requirements. Measurement of rating accuracy includes the notions of ordinal as well as cardinal accuracy. Ordinal accuracy tests the effectiveness of the ratings system in distinguishing credit risk on a relative basis. One can gauge ordinal accuracy by comparing agency ratings or default probability estimates for a common universe of obligors. Cardinal accuracy is related to the validation of risk ratings by comparing realized default rates to assumed default rates.

INDUSTRY PROFILE

The banking system remains, as always, the most dominant segment of the financial sector. Indian banks continue to build on their strengths under the regulator's watchful eye and hence, have emerged stronger.
In the current decade, this has emerged as a resurgent sector in the Indian economy. As per the McKinsey report ‘India Banking 2010’, the banking sector index has grown at a compounded annual rate of over 51 % since the year 2001, as compared to a 27 % growth in the market index during the same period. It is projected that the sector has the potential to account for over 7.7 % of GDP with over Rs.7,500 billion in market cap, and to provide over 1.5 million jobs.

In the annual international ranking conducted by UK-based Brand Finance Plc, 18 Indian banks have been included in the Brand Finance Global Banking 500 and State Bank of India (SBI), which is the first Indian bank to be ranked among the Top 50 banks in the world, has improved its position from 36th to 34th, as per the Brand Finance study released on February 1, 2011. The brand value of SBI has enhanced to US$ 1.12 billion. ICICI Bank, the only other Indian bank in the top 100 club has improved its position with a brand value of US$ 2.5 billion. Indian banks contributed 1.7 % to the total global brand value at US$ 14.74 billion and grew by 19 % in 2011, according to the study.
Nationalised banks, as a group, accounted for 51.2 % of the aggregate deposits, while State Bank of India (SBI) and its associates accounted for 22.5 %, according to Reserve Bank of India's (RBI) 'Quarterly Statistics on Deposits and Credit of Scheduled Commercial Banks: September 2010'. The share of New private sector banks, Old private sector banks, Foreign banks and Regional Rural banks in aggregate deposits was 13.5 %, 4.5 %, 5.2 % and 3.1 % respectively.
With respect to gross bank credit also, nationalised banks hold the highest share of 50.9 % in the total bank credit, with SBI and its associates at 23.1 % and New Private sector banks at 13.7 %. Foreign banks, Old private sector banks and Regional Rural banks held relatively lower shares in the total bank credit with 5.2 %, 4.5 % and 2.5 % respectively.
The report also found that scheduled commercial bank offices (with deposits of US$ 2.25 or more) accounted for 66.2 % of the bank offices, 96.6 % in terms of aggregate deposits and 93.8 % in total bank credit.
Bank loans registered a growth of 21.38 % in 2010-11, while deposit growth stood at 15.84 %, according to data released by RBI. Analysts and bankers said a growth rate of 18 % in deposits and 20 % in credit should be sustainable for banks in 2011-12.
India's foreign exchange reserves stood at US$ 308.2 billion as on April 8, 2011, according to the data in the weekly statistical supplement released by RBI.
Indians who live and work abroad have remitted US$ 55 billion in 2010 as compared to US$ 49.6 billion in 2009 and have topped the world list in sending money back home, according to World Bank's Migration and Remittances Factbook 2011. With online money transfer services provided by many banks becoming popular, remitting money from any corner of the world is no more a problem.
The last decade has seen many positive developments in the Indian banking sector. The policy makers, which comprise the Reserve Bank of India (RBI), Ministry of Finance and related government and financial sector regulatory entities, have made several notable efforts to improve regulation in the sector. The sector now compares favourably with banking sectors in the region on metrics like growth, profitability and non-performing assets (NPAs). A few banks have established an outstanding track record of innovation, growth and value creation. This is reflected in their market valuation. However, improved regulations, innovation, growth and value creation in the sector remain limited to a small part of it. The cost of banking intermediation in India is higher and bank penetration is far lower than in other markets. India’s banking industry must strengthen itself significantly if it has to support the modern and vibrant economy which India aspires to be. While the onus for this change lies mainly with bank managements, an enabling policy and regulatory framework will also be critical to their success.
Government Initiatives
The Securities and Exchange Board of India (Sebi) will address the concerns of RBI about the high share of portfolio funds in overall capital inflows as they are prone to sudden stops and reversals, while framing the guidelines for allowing foreign individual investors to invest directly in registered mutual funds. The guidelines, which will be in place by mid-May 2011, will also ensure that the subscription process is as simple as possible.
The government would provide an additional US$ 1.35 billion capital to state-owned banks in financial year 2011-12 to help them maintain at least 8 % capital adequacy ratio in Tier-I level, said the Union Finance Minister, Shri Pranab Mukherjee while presenting the Union Budget for 2011-12 (April-March) at the lower house of the Parliament.
He has also allowed fund houses to tap foreign nationals for investing in equity schemes. “To liberalise the portfolio investment route, it has been decided to permit Sebi-registered mutual funds to accept subscriptions from foreign investors who meet the KYC (Know Your Customer) requirements for equity schemes,” said Shri Mukherjee while presenting the Budget in Parliament. This would enable Indian mutual funds to have a direct access to foreign investors and widen the class of foreign investors in the Indian equity market, he added.
The government presented the Banking Laws (Amendment) Bill 2011 in the Lok Sabha. The bill proposed the following amendments among other recommendations in the existing Banking Law. • To raise the voting rights of shareholders of nationalised banks to 10 % from the existing 1 %. For private sector banks, the voting rights would be proportionate with investors’ shareholding. • To remove the voting right restriction of 10 % for private sector banks in the total voting rights of all the shareholders of the banking company. • To give powers to nationalised banks to issue two additional instruments bonus shares and rights issues to be able to get funds from capital market to expand the banking business. • To grant powers to RBI to impose such conditions as it deems necessary while granting such approval for acquisition of 5 % or more share capital of a banking company if it considers necessary. • To confer power on the RBI to call for information and returns from associate enterprises of banking companies and also to inspect the same.

RESERVE BANK OF INDIA

The Reserve Bank of India was established on April 1, 1935 in accordance with the provisions of the Reserve Bank of India Act, 1934.
The Central Office of the Reserve Bank was initially established in Calcutta but was permanently moved to Mumbai in 1937. The Central Office is where the Governor sits and where policies are formulated.
Though originally privately owned, since nationalisation in 1949, the Reserve Bank is fully owned by the Government of India.
LEGAL FRAMEWORK
Reserve Bank functions are governed by the Reserve Bank of India Act, 1934 while the financial sector is governed by the Banking Regulation Act, 1949.

RBI, being the apex bank of the country has certain functions, that include: • Monetary Authority: Formulates, implements and monitors the monetary policy with the objective of maintaining price stability and ensuring adequate flow of credit to productive sectors • Regulator and supervisor of the financial system o Prescribes broad parameters of banking operations within which the country's banking and financial system functions o The objective is to maintain public confidence in the system, protect depositors' interest and provide cost-effective banking services to the public. • Manager of Foreign Exchange • Issuer of currency • Developmental Role • Related Functions o Banker to the Government o Banker to banks

The RBI has 22 branches in India, mostly in state capitals.
RBI also has fully owned subsidiaries. They are: o National Housing Bank(NHB), o Deposit Insurance and Credit Guarantee Corporation of India(DICGC), o Bharatiya Reserve Bank Note Mudran Private Limited(BRBNMPL)

INTRODUCTION

The Basel I framework was found to have several limitations such as its broad-brush approach to credit risk, its narrow coverage confined to only credit and market risks, and non-recognition of credit risk mitigants, which encouraged capital arbitrage through structured transactions. Moreover, the rapid advances in risk management, information technology, banking markets and products, and banks’ internal processes, during the last decade, had far outpaced the simple approach of Basel I to measuring capital. A need was, therefore, felt to replace this Accord with a more risk-sensitive framework, which would address these shortcomings. Accordingly, the Basel Committee on Banking Supervision (BCBS) released on June 26, 2004 the document “International Convergence of Capital Measurement and Capital Standards : A Revised Framework”, which was supplemented in November 2005 by an update of the Market Risk Amendment. This document, popularly known as “Basel II Framework”, offers a new set of international standards for establishing minimum capital requirements for the banking organisations. It capitalises on the modern risk management techniques and seeks to establish a more risk-responsive linkage between the banks’ operations and their capital requirements. It also provides a strong incentive to banks for improving their risk management systems. The risk sensitiveness is sought to be achieved through 3 mutually reinforcing Pillars.

The Pillar 1 stipulates the minimum capital ratio and requires allocation of regulatory capital not only for credit risk and market risk but additionally, for operational risk as well, which was not covered in the previous Accord. The Pillar 1, unlike the previous Accord, provides a menu of approaches, from the simplified to the advanced ones, for determining the capital charge for each of the three categories of risks. The credit risk mitigants used by the banks have been specifically recognised to provide appropriate capital relief.

The Pillar 2 of the framework deals with the ‘Supervisory Review Process’ (SRP). It requires the banks to develop an Internal Capital Adequacy Assessment Process (ICAAP) which should encompass their whole risk universe – by addressing all those risks which are either not fully captured or not at all captured under the other two Pillars– and assign an appropriate amount of capital, internally, for all such risks, commensurate with their risk profile and control environment.

The Pillar 3 of the framework, Market Discipline, focuses on the effective public disclosures to be made by the banks, and is a critical complement to the other two Pillars. It recognises the fact that apart from the regulators, the banks are also monitored by the markets and that the discipline exerted by the markets can be as powerful as the sanctions imposed by the regulator. It is premised on the basic principle that the markets would be quite responsive to the disclosures made and the banks would be duly rewarded or penalised, in tune with the nature of disclosures, by the market forces.

PREPARATORY MEASURES FOR BASEL – II IMPLEMENTATION

Implementing the ICAAP under the Pillar 2 of the framework would perhaps be the biggest challenge for the banks in India as it requires a comprehensive risk modelling infrastructure to capture all the risks that are not covered under the other two Pillars of the framework. The validation of the internal models of the banks by the supervisors would also be an arduous task.

In regard to adoption of advanced approaches available under Basel II, the RBI has not stipulated any timeframe for adoption of these approaches but a migration to advanced approaches would certainly pose significant challenges to both – the banks as well as the supervisors. First and foremost, the banks will need to demonstrate to the supervisors that they meet the minimum criteria stipulated in the Basel II framework to be eligible to adopt the IRB approaches. This could require, inter alia, suitable adjustments in the risk-rating design and its operations for various product lines in the banks as also the governance structure to ensure the integrity of the rating process.

Second, unlike the simpler approaches under Basel II, the advanced approaches are very data intensive and require high-quality, consistent, time-series data for various borrower- and facility categories for a period of five to seven years to enable computation of the required risk parameters (such as default probability and loss given default, etc.). The banks would perhaps need a thorough review of their internal processes with a view to redesign and upgrade them to be able to capture the information needed for creating the requisite databases.

Third, a robust risk management architecture, including a strong stress-testing framework for scenario analyses, would be a necessity under the advanced approaches. A system within the banks to validate the accuracy of the internal rating processes would be an essential element of the risk management set up.

Fourth, an overarching requirement for efficient data management and for effective risk management structures, would be an state-of-the-art technological infrastructure which might need significant investment and improvement to achieve seamless enterprise-wide integrated risk management.

The minimum requirements to be met by banks relate to (a) internal rating system design, (b) risk rating system operations, (c) corporate governance and oversight, (d) use of internal ratings, (e) risk quantification, (f) validation of internal estimates, (g) requirements for recognition of leasing, (h) calculation of capital charges for equity exposures and (i) disclosure requirements.]

PREPAREDNESS OF BANKS TO IMPLEMENT ADVANCED APPROACHES OF CREDIT RISK UNDER BASEL II.

As G10 countries deregulated their financial markets and banking systems from the beginning of 1980’s onwards, one of the many side-effects was the rapid increase in the bank’s exposure to risk—both on and off the balance sheet. Generally speaking, growth was not matched with equivalent expansion in capital. As a result, the capital of large banks was eroded. Further, as banks started to expand into new business lines and increase cross-border operations, issues surrounding the creation of a level playing field among banks from different jurisdictions gained considerable importance.
The above mentioned events initiated a project to achieve better international convergence of supervisory standards for capital adequacy of internationally active banks.
The Basel Committee of Banking Supervision (BCBS) published 'International Convergence of Capital Measurement and Capital Standards' to address these issues. While the scope of the application of these standards was initially limited to large international banks incorporated in countries represented in the Basel Committee, it was ultimately applied in more than 100 jurisdictions, many of which have now adopted Basel II. In addition, in many jurisdictions, banks having solely domestic operations are subject to these standards, regardless of their size or the scope of their activities.

BASEL I

For the first time in international history of banking, there was a standard framework for the banks world over to conduct busy. Basel I provided a set of norms for capital allocation which helped banks to allocate capital to counter the risks faced by them.
It provided stability in the industry after some major bank failures and banking crisis by giving a guideline for the banks to maintain capital in order to counter unforeseen shocks.
Basel I norms helped reduce competitive distortion among banks.

Basel I norms stipulated a minimum CRAR of 8% for banks. Regulators/ Central banks could fix the CRAR above 8% to the banks within the jurisdiction based on local conditions and financial discipline.

CRAR stands for Capital to Risk Weighted Assets Ratio. It is one of the barometers to promote the stability and efficiency of financial institutions. It is also referred to as Capital Adequacy Ratio (CAR) or simply, Capital Ratio.

CRAR = Capital * 100

RWA (Credit Risk+ Market Risk + Operational Risk)

The Basel I Capital Standards are minimum regulatory capital requirements made up of 3 components: 1. Regulatory Capital List of elements that count as capital for regulatory purposes and the conditions that such element must comply with to be eligible 2. Risk Weighted Assets All credit risk exposures including off balance-sheet items converted into on balance-sheet equivalents are risk weighted by using supervisory risk weights based on the degree of risk. 3. Minimum Capital Adequacy Ratios There are 2 main capital adequacy ratios relating capital to risk-weighted assets: • Total regulatory capital divided by sum of risk-weighted assets should be at least 8% • Tier 1 Capital divided by the sum of risk weighted assets must be at least 4%

1. REGULATORY CAPITAL Regulatory capital and capital adequacy are intended to ensure that a bank can withstand major losses without causing a banking crisis that could threaten the financial system’s integrity. One of the main characteristics of the regulatory definition of capital is that eligibility criteria are prescribed in a largely similar way around the world. A large number of countries having adopted Basel I, has allowed for greater comparability and has contributed to levelling the playing field for banks internationally. These remain objectives of the Basel Committee.

The criteria against which other forms of regulatory capital are to be benchmarked are the: • permanence of capital • ability to fully absorb losses on an ongoing basis and/or the instrument’s degree of subordination • bank’s discretion on payments, meaning there can be no mandatory fixed charges (whether dividends or interest payments)
The definition of regulatory capital is currently comprised of three levels (or 'tiers') of capital. Each tier corresponds to a certain capacity for absorption of losses. An item is classified in one of these tiers if it satisfies specific criteria. TIER 1 CAPITAL - CORE CAPITAL Elements included in Tier 1 capital are deemed to have the highest capacity to absorb losses while allowing the bank to continue to operate. Common equity is eligible without any restrictions for inclusion in Tier 1 regulatory capital when it is fully paid-up and therefore immediately, permanently and fully available to absorb losses. Since common shareholders are the first to bear losses and the last to receive any proceeds in a liquidation scenario, common equity is the benchmark for all other forms of regulatory capital.
The components of Tier 1 Capital would include the following: • Paid up capital • Statutory Reserves • Other Disclosed free Reserves • Capital reserves representing surplus arising out of sale proceeds of assets • Innovative Perpetual Debt Instruments (IPDI) TIER 2 CAPITAL - SUPPLEMENTARY CAPITAL Tier 2 or supplementary capital is comprised of a broad mix of elements. However, because the ability of such elements to absorb losses is restricted, amounts to be included in Tier 2 are generally limited to a certain proportion of Tier 1. Elements that qualify for Tier 2 regulatory capital fall into one of two categories – Upper or Lower – subject to the following limits: • Total Tier 2 cannot exceed 100 percent of Tier 1 capital • Upper Tier 2, the total of which is limited to 100 percent of Tier 1 capital • Lower Tier 2, the total of which cannot exceed 50 percent of Tier 1 capital

UPPER TIER 2

1. Hybrid debt-capital instruments combine characteristics of both equity and debt. Specifications for such instruments differ from country to country but, to be eligible for Upper Tier 2, they must satisfy the following common conditions

They must be unsecured, subordinated and fully paid-up

• they are not redeemable at the initiative of the holder or without the prior consent of the supervisory authority • they are available to participate in losses without the bank being obliged to cease trading, that is, they can absorb losses on an ongoing basis • they should allow debt service obligations to be deferred where the profitability of the bank would not support payment although the obligation to pay interest may not be permanently reduced or waived

Undisclosed Reserves

Refer to a range of practices derived from national legal frameworks and accounting practices. The general qualifying criterion is that they should not be encumbered by any provision or other known liability but should be freely and immediately available to meet unforeseen future losses.

Revaluation Reserves

Two types of revaluation reserves may be included in Tier 2. • Fixed assets may be revalued to take into account changes in market values over time. These are then reflected on the bank’s balance sheet as a revaluation reserve. • Hidden values of latent reserves may be included. These arise from long-term holdings of equity securities valued at historic cost. 2. Provisions • General provisions • Specific provision

LOWER TIER 2

Subordinated Term Debt

Subordinated term debt is not permanently available to cover losses because: • it has a pre-determined maturity • it can only absorb losses in a liquidation Tier 3 capital - Additional Supplementary Capital. Additional supplementary capital consists of short-term subordinated debt instruments having certain characteristics and subject to certain conditions. The overall amount of Tier 3 capital is limited to 250 percent of a bank’s Tier 1 capital required to support market risks. Tier 2 elements may be substituted for Tier 3 capital up to the same limit of 250 percent as long as the overall limits are not breached: • Tier 2 capital cannot exceed Tier 1 capital • long-term subordinated debt cannot exceed 50 percent of Tier 1 capital
Supervisory Risk Weights and Conversion Factors A supervisory risk weight is an estimate of the credit risk associated with an exposure. Expressed as a percentage, it is used to translate the nominal amount of a credit exposure into a risk-weighted asset (RWA). This cannot be done directly for off-balance sheet items because the amount at risk for such exposures does not necessarily correspond to the nominal amount listed in the bank’s accounts. Before a risk weight can be applied, the off-balance sheet items need to be converted to credit risk equivalents by using credit risk conversion factors (CCFs).

Risk Weights and Capital Charges

Since the minimum ratio of total regulatory capital to the sum of all risk-weighted assets is 8 percent, there is a direct relationship between risk weights and capital charges. A risk weight of 100 percent corresponds to an 8 percent capital charge and as indicated in the table given below:
|Risk Weight % |Capital % |
|0 |0 |
|10 |0.8 |
|20 |1.6 |
|50 |4 |
|100 |8 |

This relationship also exists under Basel II, the revised framework for international convergence of capital measurement and capital standards.

Supervisory Treatment for Collateral and Guarantees

Under Basel I, only some types of collateralized transactions, guarantees and netting are recognized. There are also limitations within the categories themselves.
|Types |Eligibility |
|Collateral |Cash or gold |
| |Securities issued by OECD central governments or certain |
| |multilateral development banks (MDBs) |
|On-balance sheet netting |Dependant upon status of netting contract under national |
| |bankruptcy regulations |
|Guarantees |Eligible guarantors: |
| |OECD central governments |
| |OECD public sector entities (PSE’s) |
| |OECD incorporated banks |
| |Non-OECD banks where underlying transaction has residual maturity|
| |up to a year |

When a credit exposure is protected by collateral or a guarantee, the risk weight to be applied is that of the collateral or guarantor.

The advantages of Basel I norms were as follows: 1. Created clear and uniform guidelines for al banks world over. 2. It strengthened the capital base of the banks world over 3. Reduced competitive distortion among banks 4. Stability in banking sector.

However, Basel I also, has its own disadvantages: 1. It focused on a single risk—Credit Risk 2. it ignored credit risk mitigation techniques 3. Advocated use of “one size fits all” concept irrespective of the risk envisaged 4. Capital allocation was made on transaction based risk weights instead of rating based risk weights. 5. No distinction of aggressive/ moderate players 6. Overly simplified approach 7. Distortion of material credit risk in banking provided by financial innovations, like asset securitization, options etc.

In 1996, BIS came up with guidelines on Market Risk. In India, it was made effective by RBI as Asset Liability Management. Prior to these guidelines, credit risk was calculated on investment portfolio but post RBI guidelines, a risk weight of 2.5% for market risk was assigned in addition to the credit risk weight for the entire investment portfolio

Banking had changed dramatically since the Basel I document introduced in 1998. Advances in risk management and the increasing complexities of financial activities/ instruments like options hybrid securities etc prompted international supervisors to review the appropriateness of regulatory capital standards under Basel I. To meet this requirement, the Basel I accord was amended and refined, which came out as Basel II Accord.

Basel II The fundamental objective is to develop a framework that would further strengthen the soundness and stability of the international banking system. It prescribes maintaining sufficient consistency in capital adequacy regulation, which will be a significant source of competitive inequality among internationally active banks. There will be competition among banks to maintain lesser capital by way of better risk management.
The major objectives of Basel II norms are as follows: 1. Promote safety and soundness in financial system, by way of better risk assessment and capital allocation 2. To align regulatory capital to underlying risk and induce banks to strengthen their risk management capabilities 3. Incentives for enhancing risk management capabilities. Better risk management wil be rewarded by way of lesser capital requirement 4. Ensure level playing field for all banks across the globe. Basel II provides a framework for the banks world over, as a benchmark to global best practices.

Basel II differs from Basel I in the following ways: 1. In addition to credit risk, Basel II accord stipulates capital requirement for Operational Risk & Market Risk 2. A menu of approaches for computing capital charge for these risks, based on risk responsiveness of banks. Basel II norms are more risk sensitive. 3. Recognition of various types of Collaterals & Guarantors as credit risk mitigants to provide capital leverage. 4. Risk weights based on risk ratings. Higher risk weights for higher risk rated parties. 5. 3-pillar concept. Pillar 1 deals with calculation of minimum capital requirements Pillar 2 covers the supervisory review process Pillar 3 covers market discipline. These 3 pillars are mutually reinforcing in nature. Pillar 1 covers internal responsibility of the bank to maintain minimum capital Pillar 2 covers the responsibility towards the regulator, for conducting review process. Pillar 3 covers the responsibility of a bank towards various stakeholders by way of disclosures/transparency.

Basel II has gone beyond the scope of Basel I and recognized and classified risks faced by banks into 3 broad categories: • Credit Risk • Market Risk • Operational Risk

The minimum capital to risk weighted assets ratio is retained at 8% as suggested under Basel I norms. RBI stipulated a higher Capital to Risk Weighted Assets Ratio at 9% for the banks operating in India.
The importance of Pillar 1 is reflected by the fact that the capital acts as a cushion to absorb the losses arising from these risks, which on the one hand provide financial stability to the bank and on the other takes care of the reputation of the bank. If banks have adequate capital to cover the underlying risk they assume, financial stability can be ensured. Hence, banks with assets profile carrying higher risk have to maintain a higher level of capital funds

For calculating risk weighted assets for Credit, Market and Operational risk, Basel has advocated the following different approaches:
| |Credit Risk |Market Risk |Operational Risk |
|Basic Approaches |Standard Approach |Standardised Modified |Basic Indicator Approach |
| | |Duration Approach | |
|Advanced Approaches |Foundation Internal Rating Based |Internal Risk Management |Standardised Approach and Alternate |
| |Approach |Model |Standardised Approach |
| |Advanced Internal Rating Based | |Advanced Measurement Approach |
| |Approach | | |

CREDIT RISK In context of Basel II, the risk that the obligor (borrower or counterparty) in respect of a particular asset will default in full or in part on the obligations to the bank in relation to that asset is termed as Credit risk.
It is the risk of loss arising from outright default due to inability or unwillingness of the customer or counterparty to meet commitments in relation to lending, trading, hedging, settlement and other financial transaction of the customer or counterparty to meet commitments.

The risk of loss arises from 3 situations: a) Borrower/Counterparty defaults- where banks lose both interest and the principal b) Deterioration in the borrower’s credit rating—bank takes a hit if the loan is not repriced for the higher risk. c) Improvement in the borrower’s credit quality—the borrower can refinance his loan at a lower rate.

Historically, measurement of credit Risk can be done using: 1. Expert Systems Here, the judgment is made by the lending officer based upon the 5 C’s of credit—namely, Character, Capital, Capacity, Collateral and Cycle or Economic Condition. Most banks used this method. It was later withdrawn due to it its highly subjective nature. The credit decision is taken based on overall perception, the innate experience/skills/overall approach of the individual taking credit decisions.

2. Rating System Bankers and regulators use this system to ascertain the financial health of the borrower, measure credit risk before taking financial decisions or to review the performance. Each borrower account can be classified into 1 of the specified categories and each rating category has a percentage of risk weight associated with it based on which capital needs to be maintained. Some of the common rating models are Risk Assessment Model (RAM), Manual Model, Small Value Model and Portfolio Model.

3. Credit Scoring System Banks identify key factors that determine Probability of Default (i.e the chances that a borrower will not repay the dues in time) and combine them into a quantitative score. A model is developed and based upon the input data scores. Basically these are statistical models projecting percentage of default probability among a homogeneous group of accounts, which in turn helps take suitable decisions.

CREDIT RISK MEASUREMENT

STANDARDIZED APPROACH The Basel Committee as well as RBI provide a simple methodology for risk assessment and calculating capital requirements for Credit Risk under the Standardized Approach.
The Standardized Approach is divided into the following broad topics for simpler and easier understanding: • Assessment of Risk Weights • External Credit Assessments • Credit Migration

Assignment of Risk Weights In Standardized Approach, all exposures are classified into various customer types defined by Basel or RBI. Thereafter, assignment of risk weights is done, either on the basis of customer type or on the basis of the asset quality as determined by rating of the asset, for calculating the Risk Weighted Assets (RWA).
Risk weights to various borrowers/ asset class are allotted based on the risk perception of each customer/asset type, which is subject to review by the Regulator based on market conditions and risk apprehensions.

External Risk Assessments Institutions (ECAIs) recognized by the Regulator/RBI assign rates to borrowers. These rates may be used as a basis for assigning risk weights to borrowers. Better rating means better quality of assets and lesser risk weights and hence lesser requirement of capital allocation.
All on balance sheet and off balance sheet assets except assets held under Trading book (i.e investments held for trading and available for sale category) are to be taken into account for computing Risk Weighted Assets and Capital charge.
On- balance sheet items refer to those assets and liabilities that are part of the balance sheet and thus, clearly appear on the balance sheet format.
Obligations of a bank that are contingent in nature are called off-balance sheet items.
Eg: letters of credit, Bank Guarantee, Forward Contract, Derivatives etc.

Taking into account the widespread business nature of the banks and other financial institutions, Basel has segregated all customers into different groups. Each such group is known as a customer type. Each customer type under Basel II norms is a homogeneous group having identical risk perception or risk weight. This group is based upon the type of business activity and undertaken by them and the nature of exposure.various customer types as stipulated by the RBI, are as shown below: 1. Sovereigns 2. Public Sector Enterprises 3. Multilateral Development banks (MBD’s), bank for International Settlements (BIS) and Internationla Monetary Fund(IMF). 4. Banks 5. Primary dealers & Securities Firms. 6. Corporates 7. Regulatory Retail Portfolio 8. Claims secured by Residential Property 9. Claims secured by Commercial Real Estate 10. Other Specified Categories: a. Venture Capital Funds b. Capital Market c. NBFC’s d. Staff e. Consumer Credit including personal loans

1. Sovereigns – RBI classifies sovereign as a customer type and exposure to both Central and State Governments. Such exposures are usually regarded as extremely safe and attract a weight of 0% except in extraordinary circumstances. 2. Banks – Claim on banks include exposure to banks by way of deposits with banks, placements with the banks, bills discounted against LC opened by banks, guarantees issued by counter guarantees of other banks etc. Risk weightages assigned are different for scheduled and non-scheduled banks.

3. Public Sector Enterprises—are those enterprises where Government’s holding in paid up share capital is 51% or more. Claims on domestic PSE’s can be risk weighted as claims on corporates. Claims on foreign PSE’s are to be risk weighted as per the rating assigned by international rating agencies.

4. Multilateral Development Banks -- are institutions that provide financial support and professional advice for economic and social development activities in developing countries. It typically refers to the World Bank Group ( International Bank for Reconstruction and Development (IBRD) and the International Finance Corporation (IFC)) and 10 other banks. These banks usually attract a weight of 20%.

5. Primary Dealers (PD’s) – are dealers who are dedicated predominantly to securities business and in particular to Government Securities market. Claims on PD’s shall be treated as claims in Corporates.

6. Corporates – it is a body that is recognized as a separate legal entity having its own rights and privileges, and liabilities distinct from those of its members. RBI has recognized the following credit rating agencies whose ratings can be mapped by banks for the purpose of applying risk weights:

• Credit Analysis & Research Limited (CARE) • CRISIL Limited • FITCH India, • ICRA Limited

International credit agencies include: • Fitch • Moody’s and • Standard and Poor’s
Short term retail exposures are risk weighted as follows:
|Short –term ratings (Domestic Rating Agencies) |Risk Weights |
|CRISIL |ICRA |CARE |Fitch | |
|P1+ |A1+ |PR1+ |F1+ |20% |
|P1 |A1 |PR1 |F1 |30% |
|P2 |A2 |PR2 |F2 |50% |
|P3 |A3 |PR3 |F3 |100% |
|P4,P5 |A4/A5 |PR4,PR5 |F4/F5 |150% |
| Unrated |100% |

7. Regulatory Retail Portfolio – consists of the following advances where: • The maximum exposure per party should not exceed Rs 5 crores • The total annual turnover of the business of the small business business unit for the last 3 years is less than Rs crores. • Individual exposure is below 0.2% of the overall regulatory retail portfolio. Claims under Regulatory Retail Portfolio attract a risk weightage of 75% except in the case of an NPA.

8. Claims secured under Residential Property – all housing loans sanctioned against mortgages on residential property that is or will be occupied by the borrower, or that is rented. Risk weights are as follows, if the Loan to Value(LTV) ratio is not more than 75%.

|Amount of exposure |Risk Weights |
|Upto Rs 30 Lakhs |50% |
|Rs 30 lakhs and above |75% |

9. Claims secured by Commercial Real Estate – fund based and non-fund based exposures secured by mortgages on commercial real estates (office buildings, multi-purpose commercial premises, multi-family residential buildings, industrial or warehouse space, hotels, development & construction etc.) Exposure to entities for setting up SEZ’s or for acquiring units in SEZ’s, which includes real estate are also treated as Commercial Real Estate exposure. Claims secured by mortgages on Commercial Real estate will attract a risk weight of 150%.

Risk weight applicable to NPA( other than a housing loan) is based on the percentage of provision made for the outstanding amount, as follows:

|Specific Provisions (in percentage) of the outstanding |Risk weight |
|amount of the loan | |
|=20% to < 50% |100% |
|>= 50% |50% |

10. Some other guidelines given are as follows: ➢ All housing loans given to bank’s own staff against the mortgage of flat/house will attract a risk weight of 20% ➢ Other loans and advances to bank’s own staff should be included under regulatory retail portfolio and should therefore attract a 75% risk weight. ➢ Risk weightage for venture capital exposures has been increased to 150%.

CREDIT CONVERSION FACTOR Credit Conversion factor (CCF) is a percentage that is to be multiplied with the off-balance sheet exposure or Non-Fund based exposures to convert it into the credit exposures equivalents or fund based equivalents.

|Types of exposure/ Commitment |CCF |
|Direct credit substitutes eg. General guarantees of indebtedness |100% |
|(including standby LC’s serving as financial guarantees for loans| |
|& securities, credit enhancements, liquidity facilities for | |
|securitization transactions) and acceptances ( including | |
|endorsements with the character of acceptance) i.e Financial | |
|guarantees, Advance Payment Guarantees, Clean LC, Standby LC, | |
|etc. | |
|Certain transaction related contingent items ( performance bonds,|50% |
|bid bonds, warranties, indemnities and standby LC’s related to | |
|particular transactions) i.e Performance Guarantees, Bid Bond | |
|Guarantees etc | |
|Short term self liquidating term letter of credits arising from |20% |
|the movement of goods( such as documentary credits collateralized| |
|by the underlying shipments) for both issuing bank and confirming| |
|bank i.e Documentary LC’s | |
|Lending of banks’ securities or posting of securities where these|100% |
|arise out of repo style transactions | |
|Note issuance facilities and revolving/non-revolving underwriting|50% |
|facilities | |
|Commitments with certain drawdown |100% |
|Other commitments (formal standby facilities and credit lines) | |
|with an original maturity of: | |
|Upto 1 year |20% |
| | |
|Over 1 year |50% |
| | |
|Similar commitments that are unconditionally cancellable at any |0% |
|time by the bank without prior notice or that effectively provide| |
|for automatic cancellation due to deterioration in a borrower’s | |
|credit worthiness | |
|Take out Finance in the books of the taking over institution | |
|Unconditional take out finance |100% |
|Conditional take out finance |50% |

MAPPING All rating agencies use different rating grades very typical to them. For the purpose of assigning risk weights these rating grades are distributed into a standard framework based on the risk perception depicted by each rating grade. This framework has risk weights assigned to each category of risk rating grade. This process is called Mapping.
For long term and short term ratings, the mapping prescribed by RBI as:

Long-Term Ratings
|Credit Assessment of Domestic Rating Agencies |Risk Weights |
|AAA |20% |
|AA |30% |
|A |50% |
|BBB |100% |
|BB and below |150% |
|Unrated |100% |

|Credit Assessment of |Risk Weights |
|CRISIL |ICRA |CARE |Fitch | |
|P1+ |A1+ |PR1+ |F1+ |20% |
|P1 |A1 |PR1 |F1 |30% |
|P2 |A2 |PR2 |F2 |50% |
|P3 |A3 |PR3 |F3 |100% |
|P4,P5 |A4/A5 |PR4,PR5 |F4/F5 |150% |
|Unrated | |

CREDIT RISK MITIGATION Risk mitigation is a general term and engulfs in its meaning every step and initiative taken by a bank o protect itself against losses arising out of doing business.
In relation to credit risk, we can see that any step taken by the bank like obtaining guarantee of a suitable person, covering assets through insurance etc are all viewed as credit risk mitigants as they aim to help the bank in averting or minimizing losses.
As per RBI guidelines, the general principles applicable to the use of credit risk mitigation techniques are as under: ➢ No transaction in which Credit Risk Mitigation (CRM) techniques are used should receive a higher capital requirement other than an otherwise identical transaction where such techniques are not used. This means that collaterals or guarantees should not increase the capital requirement of the concerned loan exposure ➢ The effects of CRM will not be double counted. Therefore, no additional supervisory recognition of CRM for regulatory capital purposes will be granted on claims for which an issue specific rating is used that already reflects that CRM i.e if the CRM tools like collaterals and guarantees have been factored by the rating agency during the risk rating assignment, then the banks are not allowed to adjust the effect of those CRM against the exposure. ➢ Principal-only ratings will not be allowed within the CRM framework. In other words, if the rating is assigned only on the basis of the principal amount payable and not taking into consideration the interest payable, then such rating cannot be factored for mapping risk weight ➢ While the use of CRM techniques reduces credit risk, it simultaneously may increase other residual risks that include operational, legal, liquidity and market risks. Therefore, it is imperative that banks emply robust procedures and processes to control these risks.

[pic]
Basel II Accord has placed significant ‘focus’ on the need of public disclosure of all types of risks of a bank i.e credit risk, market risk and operational risk. The CRM techniques must bring to public domain the credit risk mitigation procedures by way of: ➢ Quantitative Disclosures ➢ Qualitative Disclosures

[pic]

HAIRCUTS Banks are required to adjust both the amount of the exposure to the counterparty and the value of any collateral received in support of the counterparty to take account of possible future fluctuations in the value of either, occasioned by market movements. This adjustment is referred to as ‘haircut’. There are 2 types of haircuts – haircut on exposure referred to as ‘He’ and haircut on collaterals referred to as ‘Hc’.

CURRENCY MISMATCH Currency mismatch arises when the collateral is denominated in a currency different form that in which the exposure is denominated. In these cases, the amount of the collateral is reduced by the application of a haircut, often referred to as Hfx, to take into consideration foreign exchange fluctuations.
Banks in India apply Hfx at a rate of 8%.

For a collateralized transaction, the exposure amount after risk mitigation is calculated as follows:

E*= max {0, [E x (1+He) – C x (1- Hc – Hfx)]}

Where,
E* = exposure value after risk mitigation
E = current value of the exposure for which the collateral qualifies as a risk mitigant.
He = Haircut appropriate to the exposure
C= current value of the collateral received
Hc= haircut appropriate to the collateral
Hfx = Haircut appropriate for the currency mismatch between the collateral and exposure.

INTERNAL RATINGS BASED APPROACH (IRB)

The Internal Rating Based approach (IRB) allows banks to asses their credit risk using their own models. The approach is split into two possible methods, between which a bank must choose, Foundation and Advanced.

The goal of modelling credit risk is to determine the credit loss distribution. A credit loss is a loss due to debtors who fail to meet their payment obligations in one year. The distribution is a combination of probabilities and losses. For instance:
There is a probability of 2% for a credit loss of €50,000.- or less.
There is a probability of 7% for a credit loss of €100,000.- or less.
There is a probability of 16% for a credit loss of €150,000.- or less.
There is a probability of 31% for a credit loss of €200,000.- or less.
Etc.
These probabilities continue to grow until it is one. The probability of an endless credit loss or less is one. This is because all credit losses are endless or less.

If there are sufficient estimates of a probability for a loss of X or less a graph can be drawn. Such a graph is called a cumulative distribution function (CDF). The following example shows ten combinations of probabilities and credit loss. Each probability indicates the probability of the associated credit loss or less.

| | |
|Probability |Credit loss (or less) |
|2% |€ 50,000 |
|7% |€ 100,000 |
|16% |€ 150,000 |
|31% |€ 200,000 |
|50% |€ 250,000 |
|69% |€ 300,000 |
|84% |€ 350,000 |
|93% |€ 400,000 |
|98% |€ 450,000 |
|99% |€ 500,000 |

These probabilities and associated losses translate to the following CDF:

[pic]

The CDF graph shows the probability for each level of loss or less. This graph can be transformed into a distribution function by taking its derivative. This strips the “Cumulative” from the CDF. From the example, the loss distribution curve would look as follows:

[pic]

The first graph shows the probability of a certain loss or less. The second graph shows the probability of a specific loss. A point on the first graph (the CDF) can be reconstructed from the second graph by adding all the probabilities equal to, or less than, the credit loss.

The purpose of holding capital is to ensure that a bank is capable of absorbing loss in an extreme situation. Basel defines an extreme situation as the point on the loss distribution with 99.9% probability of the associated credit loss or less. This is represented by the green and red area of the following graph.

[pic]

The green and red area combined make up 99.9% of the total area under the graph. In our example the point which represents the 99.9% probability is €559,025. This means there is a probability of 99.9% that the credit risk will be is €559,025 or less.

The green area in the graph represents 50% of the total area under the graph. In other words there is a 50% probability of this loss or less. This level of loss is called the expected loss. In our example the expected loss is €250,000. In other words there is a 50% probability for a loss of €250,000 or less. This part of the risk of loss should be covered by the provisions of a bank. This means that the banks provisions forcredit risk should equal the expected loss.

The red area is the remainder between the expected loss and the loss at the 99.9% point. This remainder is called the unexpected loss. The regulatory capital is used to cover the unexpected loss. In our example we showed that the 99.9% point was at a loss of €559,025 and the expected loss was €250,000. Therefore the unexpected loss in our example is €559,025 - €250,000 = €309,025.
The sum of the provisions and the regulatory capital should equal the 99.9% loss. If for some reason the provisions are less than the expected loss, it should be compensated by holding extra regulatory capital.

Vasicek model

The formula used to determine the regulatory capital is commonly referred to as the Vasicek model. The purpose of this model is to determine the expected loss (EL) and unexpected loss (UL) The first step in this model is to determine the expected loss. This is the average credit loss.. The expected loss is determined using three main ingredients:
PD: Probability of default, the average probability of default over a full economic cycle;
EAD: Exposure at default, the amount of money owed at the moment a counterparty goes into default;
LGD: Percentage of the EAD lost during a default.
The expected loss (EL) is equal to the PD times the LGD times the EAD:

EL = PD X LGD X EAD

The expected loss is half the work of the model. The EL determines (roughly) the amount of provisions which should be taken.
The second half of the work is to determine the Unexpected Loss(UL). The UL is the additional loss in atypical circumstances, for which tier capital should be retained. The Vasicek model estimates the UL by determining the PD in a downturn situation. The model assumes that the EAD and LGD are not affected by dire circumstances. Both parameters are considered constant for a company. The model calculates the loss during a downturn situation (for instance an exceptionally bad economy) by multiplying the downturn PD times the LGD times the EAD. The UL is calculated by subtracting the expected loss from the loss during a downturn situation. In formula’s this equates to:

UL = (PDdownturn X LGD X EAD) – (PD X LGD X EAD),

which is equal to:

UL = (PDdownturn – PD) X LGD X EAD

The PD in a downturn situation is determined using the average (through the cycle) PD. At this point Vasicek uses two different models. First it uses the Merton model. This model states that a counterparty defaults because it cannot meet its obligations at a fixed assessment horizon, because the value of its assets is lower than its due amount.

If the asset value drops below the total debt, the company is considered in default. This logic allows credit risk to be modelled as a distribution of asset values with a certain cut-off point (called a default threshold), beyond which the company is in default. The area under the normal distribution of the asset value below the debt level of a company therefore represents the PD. The following figure shows a normal distribution of the assets values. The current asset value of this example is €1,000,000, the standard deviation is €200,000 and the total debt is €700,000. The probability of the asset value falling below €700,000 (the total debt level and therefore the default threshold) is equal to the area red area in the graph. As a company is considered in default if the asset value drops below the total debt, this probability is equal to the PD. In our Example the red area (PD) is 6.68%.

[pic]

The logic used by Merton (shown in the graph above) can also be reversed. In Vasicek a PD (for instance calculated with a scorecard) is given as input. Instead of taking the default threshold (debt value) and inferring the PD as Merton does, Vasicek takes the PD and infers the default threshold. Vasicek does this using a standard normal distribution. This is a distribution with an average of zero and a standard deviation of one. This way the model measures how many standard deviations the current asset value is higher than the current debt level. In other words it measures the distance to default. The graph below shows that a PD of 6.68% means that the company is currently 1.5 standard deviations of its asset value away from default. By using the standard normal distribution the actual asset value, standard deviation and debt level becomes irrelevant. It is only necessary to know a PD and the distance to default can be determined.

Now that the PD has been transformed to a distance to default the second step of the model comes into play. In this step Vasicek uses the Gordy model. The distance to default is a through the cycle distance, because the PD used is through the cycle. In other words it is an average distance to default in an average situation. This distance to default (-1.5 in our example) will have to be transformed into a distance to default during an economic downturn. To do this a single factor model is used. It is assumed that the asset value of a company is correlated to a single factor. In other words, if the factor goes up the asset value goes up, if the factor goes down the asset value goes down. This factor is often referred to as the economy. This is done because it is intuitively logical that the asset value of a company is correlated to the economy. However the factor is merely conceptual. It is assumed that there is a single common factor (whatever it may be) to which the asset value of all companies show some correlation. The common risk factor (the economy) is also assumed to be a standard normal distribution.

To recap we have a standard normal distribution representing the possible asset values, a default threshold inferred using the PD (-1.5 in our example), a standard normal distribution representing the economy to which the asset value is correlated and a correlation between the economy and the asset value. Using the correlation it is possible to determine the asset value distribution given a certain level of the economy. If the economy degrades the expected asset value will also decrease shifting the asset value distribution to the left. Furthermore the standard deviation will also decrease. In other words an asset value distribution given a certain level of the economy can be calculated using the correlation between the asset value and the economy. The following graphs give an example of how the asset value distribution can change as the economy level decreases.

[pic]

[pic]

As the asset value distribution shifts the distance to default also shifts (decreases). The graphs below show the effect on the PD. The increase in the red area (and decrease in the distance to default) represents the increase in the PD due to adverse economic conditions.

[pic]

The degree in which the asset value distribution is deformed depends on the level of the economy which is assumed. The level of the economy is measured as the number of standard deviations the economy is from the average economy. For instance the economic level with a probability of 99.9% of occurring or better has a distance of 3.09 standard deviations from the average economy.

The new distance to default can be calculated by taking the average of the distance of the level of the economy (used to determine the downturn PD) and the distance to default, weighted by the correlation. In formula’s this equates to:
Distance To Default Downturn = (1/(1-r))^-0.5 X Distance To Default+ (r/(1-r))^0.5 X Distance From Economy.

In our example the PD was 6.68% and the distance to default was -1.5. Assuming a counterparty has a 9% correlation to the economy. Secondly determine that the economic downturn level is the 99.9% worst possible economic level (used in BIS II). At this level the distance between the downturn level and the average economy is 3.09. In our equation the new distance to default (given the 99.9% worst economy) is:
-0.46 = (1/(1-9%))^-0.5 X -1.5 + (9%/(1-9%))^0.5 X 3.09
In other words the -1.5 distance to default decreases to a distance to default of -0.46. The new PD associated with a distance to default of -0.46 is 32.31%.

PROBABILITY OF DEFAULT

The probability of default (also call Expected default frequency) is the likelihood that a loan will not be repaid and will fall into default. PD is calculated for each client who has a loan (for wholesale banking) or for a portfolio of clients with similar attributes (for retail banking). The credit history of the counterparty / portfolio and nature of the investment are taken into account to calculate the PD.

PD is always expressed in percentage of number of accounts.
The PD’s are validated and monitored on a continual basis. The validation process involves 2 aspects:
A Model Validation: An analysis of the rating model’s ability to rank the customers and separate them into different risk grades.
Validation of the PD level: a comparison of the observed PD level and the estimated PD level. The comparison is done on the basis of confidence intervals.

The PD is estimated using rating models developed by banks. PD per rating grade gives the average percentage of borrowers that default in any rating grade over the course of one year.
For computing PD, the credit history of the borrower/ portfolio and nature of the investments are taken into account.
There are many alternatives for estimating the PD. Default probabilities may be estimated from a historical database of actual defaults using modern techniques like logistic regression. Default probabilities may also be estimated from the observable prices of credit default swaps, bonds and options on common stock.
The simplest method adopted by many banks, is to use internal or external ratings for estimating PD’s from historical default experiences.

1 year PD for a rating grade ‘I’= number of borrowers with rating ‘I’ ( at the beginning of the given time period) that defaulted during the given time period. Total number of borrowers with rating grade ‘I’ at the beginning of the given time period.

Basel II norms require the banks to estimate 1 year PD’s based on long term averages (a minimum of 5 years’ average). This can be done by generating yearly borrower pools, where each borrower is placed according to the rating at the beginning of the year, and taking average of the pools.

Credit Scoring Models
There are statistical models based on historical data to determine between good and bad exposures. In other words, they use Discriminant Methodology. Some examples are Altman’s Z-Score; Linear probability Model; Logit Model.
Based on the model, PD or implied Pd is calculated:

• Structural Models/ Option Theoretic Models/ Market Based Models o It is a factor based approach, the factor being the market value of firm’s assets. The value of the firm’s assets falling below the value of fixed liabilities results in a default. PD is determined by the dynamics of the assets. o Based on Distance to default, Expected Default Probability is mapped using normal distribution.

LOSS GIVEN DEFAULT

The Loss Given Default (LGD) is one of the three main ingredients in the Basel model. It represents the percentage of the Exposure at Default (EaD) which you expect to lose if a counterparty goes into default.
To model the LGD it is important to look at what happens after a counterparty goes into default.
There are several scenario’s of events which may occur after a company goes into default. The two most extreme are as follows:
- The counterparty recovers without any loss to the bank;
- Sale of assets and collateral is required.
There are also scenario’s in-between these two extremes with various possible associated losses. The finance could be restructured (a new term structure for instance) or the exposure could be sold to another bank.

Full Recovery
Because the definition of default is rather strict (90 days overdue) many defaults will fall in the first category. Most companies who are 90 days overdue simply recover. Often even without intervention by bank.

Sale of assets and collateral
The sale of assets and collateral occurs less frequently but leads to higher losses. It can be assumes that this scenario only occurs when a company goes bankrupt. Note that bankruptcy is a lot worse than default (minimally 90 days overdue). Generally you can separate the returns in two types:
- Return on collateral
- Return on unpledged assets
Collateral are the assets which the customer has pledged. There is an agreement that the proceeds from the sales of the assets will be used to repay the loan.
Unpledged assets are the assets not pledged to anyone. The proceeds from the sales of these assets will be distributed first among the preferred creditors, second among the senior creditors (this is determined in the loan specifications) and third among the subordinated creditors (again this is determined in the loan agreement).

Besides the actual amount retrieved it is also important to consider the time it takes and the costs you will have to make. Basically money now is better than money later and easy money is better than money which takes a lot of effort. Both these factors (time and effort) together with the actual retrieved amount determine the return.

Modeling LGD

As mentioned before the scenarios after default can be split into three groups:
- Full recovery;
- Sale of assets and collateral;
- In-between scenario’s

The first having zero (or almost zero) loss. The second having a loss depending on the amount of assets. This translates to a large amounts of defaults with zero loss, a smaller amount of defaults with a loss of 100% and the remaining defaults in-between. This is graphically represented below

[pic]

As the graph suggests the most important part will be to determine how to identify the counterparties which will recover. The probability of recovery is perhaps the most important part of the model. After determining the probability of a full recovery the Loss in the remaining situations may be modeled.
This can be done using a simple model which relates a few factors directly to the expected loss if not recovered. For instance senior secured debt in a certain industry has a loss of X% if it does not recover fully. Important factors are: seniority, collateral (amount and quality) and jurisdiction. The effect can be directly modeled using regressions between the observed losses (of defaults which do not recover fully) and the value of the factors. This model can be combined with the model for recovery. In other words, the model for recovery gives a probability of zero loss. One minus this probability times the LGD model excluding recovery gives the total LGD model.

LGD = PR X zero loss + (1-PR) X average loss if not recovered

Where PR stands for the Probability of Recovery.

A more elaborate approach identifies the remaining scenarios (sale of assets and in-between scenarios) separately. The 1st step would be to determine what the probability of the asset sales scenario is and how much will be recovered. Determine expected returns on asset types and an expected time it takes before money can be recovered. Expected proceeds must be discounted using the expected time until receiving the money and a discount rate. Issues to take into account are the mentioned jurisdictions, collateral versus non collateral assets and covenants. The latter is a difficult issue. A covenant allows the bank to request collateral if a counterparty’s credit worthiness diminishes. This means that the value of a covenant is dependant on the bank’s ability to identify counterparties in trouble and its ability to negotiate collateral once it becomes needed. The remainder is all the scenario’s in-between. This is more difficult. It is not possible to model all possible scenarios and all possible losses associated with them. It can be assumed that the expected loss lays somewhere in-between the two extreme scenarios. The probability of the three scenarios should add up to 100%.

EXPOSURE AT DEFAULT
A total value that a bank is exposed to at the time of default. Each underlying exposure that a bank has is given an EAD value and is identified within the bank's internal system.
As a company goes towards default it will normally attempt to increase its leverage (lend more). This is logical because the reason for default is generally a liquidity problem. The EaD model will thus look at the company’s ability to increase its exposure while approaching default. The degree in which this is possible will be dependant on the type of products (facilities) the company has and the bank’s ability to prevent excessive draw down on facilities. The products can be separated into three main categories.
1. Loans
2. Working capital facilities.
3. Potential exposures

Loans
Loans are products where the money is made available at predetermined moments and the customer is required to repay at predetermined moments. Therefore there is very little the company can do to increase the debt. Modeling these products is easy. The entire Expected Loss(EL) system calculates the Expected Loss in one year. Thus the Probability of Default (PD) is the probability that a customer goes into default in the next year. This means that on average the time until default will be six month’s. Therefore in order to calculate the Exposure at Default, simply add all scheduled payments to the customer and subtract all the scheduled repayments by the customer in the next six month’s. Because a default occurs once there is a failure to pay for 90 day’s (three month’s), you may assume that the repayments and interest payments in the last 90 days where not made. This leads to the following logic:
EaD = The Current exposure + scheduled payments next 6 months - scheduled repayments next 3 months + 3 months of missed interest payments.

Working capital facilities

A working capital facility is used by a company to manage their liquidity. The facility allows the company to borrow money up to a pre-set limit. The customer is free to borrow and repay any amount at any time as long as the total exposure remains below the limit. This freedom makes it a bit more difficult to model the EaD. It can be expected that the customer will increase its exposure as it moves towards default.
In some cases, the customer may even increase exposure beyond the prescribed level. To be prudent the EaD could be set equal to the limit (or exposure if this is already over the limit). If the bank accepts draw downs beyond the limit an add-on could be applied (for instance 105% times the limit or exposure if this is already over the limit). The add-on can be determined by dividing the measured Exposure at Default by the limit, for each defaulted customer and averaging the outcomes. The model is shown graphically below. In the example the exposure is 50, the limit is 100 and the EaD is 105. This means the add-on is 5%. [pic]

If the average measured Exposure at Default is less than the limit you can use a different model. The model should take into account the current exposure to the counterparty. This ensures that the calculated EaD does not drop below the current exposure. The counterparty will withdraw an additional amount ending up with an exposure at default somewhere in-between its current exposure and the limit. In other words the customer will withdraw a percentage of his remaining borrowing room (the difference between the current outstanding and the limit).
If a counterparty has gone into default first determine the borrowing room six month prior to default (the average time to default). Then determine what percentage of the borrowing room was drawn at default. This percentage can take on extreme values as the borrowing room increases and even be incalculable if the borrowing room is zero (because you can’t divide by zero). These extreme values should be excluded when calculating the average use of the borrowing room. The graph below depicts this model. [pic]

In the example the current exposure is 50, the limit is 100 and the EaD is 80. This means that the borrowing room is 50 (limit minus exposure). The usage of the borrowing room is 60%. This is calculated by
Borrowing room usage = (EaD-current exposure)/Borrowing room.

Potential exposures
These are products which might lead to an exposure. An example is a guarantee. The bank gives a guarantee for the customer to a third party. This guarantee will only translate into an exposure (on the customer) if this third party requests payment under the guarantee. Only a certain percentage of third parties will do so. To determine the exposure equivalent of a potential exposure you can use the Cash Conversion Factors (CCF) given in the standardized approach.
Some of these products are sold in a structure similar to a working capital facility. This means the customer may use guarantees from the bank up to a certain limit. In this case the working capital model can be combined with the CCF.

Model Evaluation

The Basel Committee on Banking Supervision highlights the relatively informal nature of the credit model validation approaches at many financial institutions. Data sufficiency and model sensitivity analysis are significant challenges to validation.
The techniques are quite general and can be used to compare a variety of model types.
The cumulative accuracy profiles (CAPs) can be used to make visual qualitative assessments of model performance.
To plot a CAP, one first orders companies by their model score, from riskiest to safest. For a given fraction x of the total number of companies, a CAP curve is constructed by calculating the percentage y(x) of the defaulters whose risk score is equal to or lower than the one for x. Obviously, a good model concentrates the defaulters at the riskiest scores and, therefore, the cumulative percentage of all defaulters identified on the y axis increases quickly as the companies with the highest risk score are considered. If the model-assigned risk scores randomly, we would expect to capture a proportional fraction of the defaulters with about x% of the observations, generating approximately a straight line or random CAP.
On the other hand, a perfect model would produce the ideal CAP curve, which is a straight line capturing 100% of the defaults within a fraction of the population equal to the fraction of defaulters in the sample. Because the fraction of defaulters is usually a small number, the ideal CAP is very steep.
The following figure shows three CAP curves for a portfolio with a fraction of approximately 10% defaulted firms. Hereby, CAP curves for a random model, an exemplary scoring model, and the perfect model are provided. Obviously, one of the most useful properties of CAPs is that they reveal information about the predictive accuracy of the model over its entire range of risk scores for a particular time horizon. For the exemplary model in the figure, we find that among the 10% of companies with the highest risk score, approximately 35% of the defaulted firms were identified, while approximately 60% of defaulted companies were classified within the group of the 20% with highest risk scores. This kind of information may be particularly helpful to interpret the quality of a rating system with respect to different intervals of the scores.
The greater the area between the model power curve and the random power curve, the better is the overall performance of the model. The maximum area that can be enclosed above the random power curve is identified by the ideal power curve. Therefore,the ratio of the area between a model’s power curve and the random power curve to the area between the ideal power curve and the random power curve summarizes the predictive power over the entire range of possible risk values

AR = A − Arandom Aperfect − Arandom
This measure is called the accuracy ratio (AR), which is a fraction between 0 and 1. Obviously, values of the AR close to 0 display little advantage over a random assignment of risk scores, while those with AR values near1 display almost perfect predictive power.
Mathematically, the AR value can be calculated according to [pic] 1
AR =2 y(x)dx − 1 0 1 − f
Hereby, y(x) is the power curve for a population x of ordered risk scores, and f = D/(N +D) is the fraction of defaults, where D is the total number of defaulting obligors and N is the total number of non defaulting obligors.
The AR is a global measure of the discrepancy between the power curves.

Gini Coefficients are widely used to measure the degree of concentration, or inequality, of a variable in a distribution of its elements. In the case of credit analysis, it is measuring the ability of a predictive measure to discriminate between defaulting and non-defaulting companies. BANK A

Bank A has, in accordance with Basel and RBI guidelines has introduced an internal rating system to evaluate credit risk. The bank uses a framework given by CRISIL. The software used is called CRISIL Risk & Infrastructure Solutions Ltd (CRIS).

The procedure followed in granting loans takes the following form: • The borrower approaches the bank and presents his case for a loan approval • The loan manager after assesses the credentials of the prospective borrowers. He is required to answer questions relating to the borrower. The answers are in the form of scores, with 10 being the highest and 0 being the lowest. The loan manager uses his sense of judgement, supported by other documentary evidence to score the borrower. • Each score is given a weight. An aggregate weighted score is computed by the software and then rated accordingly. For example, a borrower with a high aggregate score of 9 will be classified into the SYND 1 rating, which is of least risk. • Bank A’s own rating system ranges from SYND 1 to SYND 10 with SYND 1 being the highest and SYND 10 being the lowest. • All loan and borrower related information is entered in a note and sent to the next level of checks. • Bank A follows the Maker and Checker policy, where the loan manager plays the role of the Maker and subsequent checkers, verify the ratings given by the loan manager. Checking is done at different levels depending on the value of the credit. For example, if the credit value is greater than 20 lakhs, it is verified by the Regional Office; and if it is greater than 2 crores, it is verified by the Central Office. • The evaluation process can neither be completely subjective nor objective. Hence the software has framed questions that combine both the aspects. • There are also asset classes and companies are classified accordingly. Weightages of various aspects vary depending on the asset class it belongs to. • There are 11 Corporate Rating Models and 6 Retail Rating Models: The 11 Corporate Asset classes include the following: 1. Banks 2. Infrastructure – Port 3. Infrastructure -- Power 4. Infrastructure -- Road 5. Large Corporate 6. Large Trader 7. Real Estate 8. SME – Manufacturer 9. SME – Service 10. SME – Trader 11. NBFC’s

The 6 Retail Rating Models: 1. Agricultural Loan 2. Clean Loan 3. Educational Loan 4. Home Loan 5. Secured Commercial Loan 6. Secured Loan.

Companies are further classified and rated based on the following:
• Greenfield Projects
• Company with Project
• Company without Project

Ratings for companies are done by taking into consideration a variety if factors that can be broadly categorized under one of the following 2 heads: 1. Company Rating 2. Project Rating

Company Rating Comprises of: • Industry Risk – this is Head Office administered since industry risk is common to all firms. This value cannot be changed by any other branch or official. • Business Risk – studies and rates factors like capacity utilization, employee turnover etc. • Management Risk – rates the efficiency of the top level management of the company • Financial Risk – the software emphasizes on 11 financial ratios extracted and calculated from the financials that have been fed into the profile of the company.

Project Rating Comprises of: • Construction Risk – rates the project during the course of its implementation • Funding Risk • Business Risk

The bank was unwilling to share any information with respect to delinquencies associated with its rating classes. The following, however, are dummy records that were submitted to CRISIL.

Validation of the internal rating system at Bank A Rating wise distribution of Domestic Fund Based Exposures

|Rating Grade |September 2010 |% to Total |March 2010 |% to Total |Ceiling as per policy |
|SYND 1 |5689 |6.61% |4590 |5.56% | Not |
|SYND 2 |6189 76.16% |7.19% |8047 |9.74% 77.6% | Below |
|SYND 3 |25547 |29.67% |22245 |26.93% | 50% |
|SYND 4 |28157 |32.70% |29217 |35.37% | |
|SYND 5 |12980 |15.07% |11156 |13.51% 19.09% | Not |
|SYND 6 |3379 20.39% |3.92% |3437 |4.16% | Exceeding |
|SYND 7 |1202 |1.40% |1175 |1.42% | 40% |
|SYND 8 |289 |0.34% |328 |0.40% | Not |
|SYND 9 |366 0.95% |0.43% |218 |0.26% 0.88% | Exceeding |
|SYND 10 |163 |0.19% |181 |0.22% | 5% |
|Default |2145 |2.49% |2005 |2.43% |Not exceeding 5% |

Rating wise distribution of Domestic Non-Fund based Exposures
|Rating Grade |September 2010 |% to Total |March 2010 |% to Total |Ceiling as per policy |
|SYND 1 |880 |8.97% |824 |8.91% | Not |
|SYND 2 |2230 89.31% |22.73% |2706 |29.24% | Below |
|SYND 3 |3503 |35.7% |3298 |35.64% | 50% |
|SYND 4 |2149 |21.9% |1646 |17.79% | |
|SYND 5 |505 |5.15% |440 |4.76% | Not |
|SYND 6 |352 9.36% |3.59% |200 |2.16% | Exceeding |
|SYND 7 |61 |0.62% |7 |0.08% | 40% |
|SYND 8 |0 0.07% |0.00% |0 |0.00% | Not |
|SYND 9 |0 |0.00% |0 |0.00% | Exceeding |
|SYND 10 |7 |0.07% |14 |0.15% | 5% |
|Default |124 |1.26% |118 |1.28% |Not exceeding 5% |

It is clear from the above 2 tables that the bank has given out credit well within its limitations and ceilings.
They carry out a continued review process to ensure that all policies and ceilings have been complied with.

1 year PD for a rating grade ‘I’= number of borrowers with rating ‘I’ (at the beginning of the given time period) that defaulted during the given time period.

Total number of borrowers with rating grade ‘I’ at the beginning of the given time period.

|Credit Rating | April ’10 – Dec ‘10 |
| |Total Number |Default Accounts |PD |PD % |
|SYND 1 |20 |0 |0 |0 |
|SYND 2 |248 |0 |0 |0 |
|SYND 3 |754 |3 |0.00397878 |0.397877984 |
|SYND 4 |711 |3 |0.004219409 |0.421940928 |
|SYND 5 |461 |2 |0.004338395 |0.433839479 |
|SYND 6 |194 |0 |0 |0 |
|SYND 7 |30 |0 |0 |0 |
|SYND 8 |3 |0 |0 |0 |
| |2421 |8 |0.00330442 |0.330441966 |

[pic]

|Credit Rating |April ’09- March’10 | | |
| |Total Number |Default Accounts |PD |PD % |
|SYND 1 |25 |0 |0 |0 |
|SYND 2 |231 |1 |0.004329004 |0.432900433 |
|SYND 3 |743 |2 |0.00269179 |0.269179004 |
|SYND 4 |648 |4 |0.00617284 |0.617283951 |
|SYND 5 |404 |7 |0.017326733 |1.732673267 |
|SYND 6 |142 |8 |0.056338028 |5.633802817 |
|SYND 7 |24 |4 |0.166666667 |16.66666667 |
|SYND 8 |3 |0 |0 |0 |
| |2220 |26 |0.011711712 |1.171171171 |

[pic] Figure 1-- Probability of Default (April '09-March'10)

All though the bank started off well with the implementation of the internals ratings system, there are some flaws in the system so enforced. As can the observed, for the year april’09 to March ’10, the probability of default is highest for the SYND 7 rating, which is expected as SYND 7 signifies low credit rating.

However, the graph of April’10 to March ’10 shows the highest PD at SYND 5 along with a smaller peak at SYND 4. this means the method is defaulted since SYND 4 and 5 are considered reasonably safe.

Average PD for the period April ’09 to December ’10.

|PD % (April 09-March'10) |PD % (April 10-Dec'10) |Average PD % |
|0 |0 |0 |
|0.432900433 |0 |0.216450216 |
|0.269179004 |0.397877984 |0.333528494 |
|0.617283951 |0.421940928 |0.519612439 |
|1.732673267 |0.433839479 |1.083256373 |
|5.633802817 |0 |2.816901408 |
|16.66666667 |0 |8.333333333 |
|0 |0 |0 |
|1.171171171 |0.330441966 |0.750806569 |

[pic]

The average PD curve is a normal one with the peak at SYND 7. This shows that there is consistency in the internal rating system, that is analyzed over a period of time

External rating of Corporates
| |Balance | % to Total |
|Total Exposure |95917 |100 |
|Rated Exposure |39369 |41.07 |
|Unrated Exposure |56521 |58.93 |

Rating at the time of Restructuring/ Default

|Credit Ratings |2007 |2008 |2009 |Total |
|SYND 1 | | |2 |2 |
|SYND 2 | |1 |7 |8 |
|SYND 3 | |4 |33 |37 |
|SYND 4 | |5 |35 |40 |
|SYND 5 | |10 |36 |46 |
|SYND 6 |1 |4 |29 |34 |
|SYND 7 |1 |5 |13 |19 |
|SYND 8 |1 |2 |- |3 |

|External Grade |SYND Grade (based on actual |CRISIL Case Mapping |Deviations |
| |PD) | | |
|AAA |1 |1 |- |
|AA+ |1 |1 |- |
|AA |1 |2 |D |
|AA- |1 |2 |D |
|A+ |1 |3 |D |
|A |2 |4 |D |
|A- |2 |4 |D |
|BBB+ |3 |5 |D |
|BBB |4 |5 |D |
|BBB- |5 |6 |D |
|BB+ |5 |7 |D |
|BB |6 |7 |D |
|BB- |6 |8 |D |
|B+ |6 |8 |D |
|B- |7 |9 |D |
|C |8,9 |9 |- |
|D |10 |10 |- |

The above table further shows that the internal mapping system is also flawed. It is not mapped as per the ideals set forth by CRISIL, whose software they follow. Hence, there is more scope for errors in lending, leading to higher defaults.

CAP Curve

[pic]

SUGGESTIONS: • Developing a more scientific mapping procedure • Setting up of committees that look into the process put in place to implement advanced tools • Appointing independent officers and supervisors, well-versed Internal Ratings Based (IRB) Approach for effective implementation.

BANK B

Bank B, like Bank A uses a framework prescribed by CRISIL.
Risks at Bank B can be braodly classified into 2 categories: 1. Retail exposure These exposures comprise of: • Home loans • Vehicle loans • Personal loans • Credit card loans • Educational loans • Agriculture-crop loans • Agriculture sector loans • Government sponsored schemes • SHG 2. Non-Retail Exposures. This category comprises of: • Large corporate ■ qualifies as a large corporate only if company has a turnover of Rs 50 Crore or more ■ can be a Greenfield project, a company with project, or a company without a project • Large Trader ■ turn-over must be less than Rs 50 Crore • Bank • NBFC • SME (Manufacturing) • SME ( Traders) • SME (Services) • Real Estate Developers • Brokers • Infrastructure Projects (port) • Infrastructure Projects (road) • Infrastructure Projects (telecom) • Infrastructure Projects (Power) • Infrastructure Projects ( Airports) • Micro-financing Institutions • Sovereign Ratings • Object Finance.

|Particulars |Rating Models |
|Retail loans |Retail Model of CRISIL |
|Non-retail loans < Rs 1 Crore |Manual of bank customised by CRISIL |
|Non-retail 1cr – 7.5 crs |CRISIL Model (RAM) |
|Non-retail